





import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

__UpperCAmelCase							=   logging.getLogger()
def       __UpperCamelCase					(      )						->   str:
    '''simple docstring'''





    lowerCAmelCase_       :						Optional[int]							= argparse.ArgumentParser()
    parser.add_argument("""-f"""		)
    lowerCAmelCase_       :						Optional[int]							= parser.parse_args()
    return args.f
def       __UpperCamelCase					(      lowercase__						:							Optional[int]		)						->   Optional[int]:
    '''simple docstring'''





    lowerCAmelCase_       :						Any							= {}
    lowerCAmelCase_       :						List[str]							= os.path.join(UpperCamelCase_		,     """all_results.json"""		)
    if os.path.exists(UpperCamelCase_		):
        with open(UpperCamelCase_		,     """r"""		) as f:
            lowerCAmelCase_       :						Optional[Any]							= json.load(UpperCamelCase_		)
    else:
        raise ValueError(f'can\'t find {path}'		)
    return results
def       __UpperCamelCase					(      )						->   List[Any]:
    '''simple docstring'''





    lowerCAmelCase_       :						Tuple							= torch.cuda.is_available() and torch_device == """cuda"""
    return is_using_cuda and is_apex_available()


__UpperCAmelCase							=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class 		__a				(						snake_case__   ):



       @classmethod
       def 			A				(  cls						: Tuple      ):
           # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
           lowerCAmelCase_       :						Optional[int]							= tempfile.mkdtemp()
           lowerCAmelCase_       :						str							= os.path.join(cls.tmpdir       ,      """default_config.yml"""      )
           write_basic_config(save_location=cls.configPath      )
           lowerCAmelCase_       :						Tuple							= ["""accelerate""", """launch""", """--config_file""", cls.configPath]



       @classmethod
       def 			A				(  cls						: Optional[Any]      ):
           shutil.rmtree(cls.tmpdir      )



       @mock.patch.dict(os.environ       ,      {"""WANDB_MODE""": """offline"""}      )
       def 			A				(  self						: List[str]      ):
           lowerCAmelCase_       :						str							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase_       :						int							= F'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

           if is_cuda_and_apex_available():
               testargs.append("""--fp16"""      )

           run_command(self._launch_args + testargs      )
           lowerCAmelCase_       :						Union[str, Any]							= get_results(_UpperCAmelCase      )
           self.assertGreaterEqual(result["""eval_accuracy"""]       ,      0.75      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """epoch_0"""      )      )      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """glue_no_trainer"""      )      )      )



       @mock.patch.dict(os.environ       ,      {"""WANDB_MODE""": """offline"""}      )
       def 			A				(  self						: Optional[int]      ):
           lowerCAmelCase_       :						Optional[int]							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase_       :						List[Any]							= F'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

           if torch.cuda.device_count() > 1:
               # Skipping because there are not enough batches to train the model + would need a drop_last to work.
               return

           run_command(self._launch_args + testargs      )
           lowerCAmelCase_       :						Tuple							= get_results(_UpperCAmelCase      )
           self.assertLess(result["""perplexity"""]       ,      1_00      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """epoch_0"""      )      )      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """clm_no_trainer"""      )      )      )



       @mock.patch.dict(os.environ       ,      {"""WANDB_MODE""": """offline"""}      )
       def 			A				(  self						: str      ):
           lowerCAmelCase_       :						Dict							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase_       :						Union[str, Any]							= F'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

           run_command(self._launch_args + testargs      )
           lowerCAmelCase_       :						Tuple							= get_results(_UpperCAmelCase      )
           self.assertLess(result["""perplexity"""]       ,      42      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """epoch_0"""      )      )      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """mlm_no_trainer"""      )      )      )



       @mock.patch.dict(os.environ       ,      {"""WANDB_MODE""": """offline"""}      )
       def 			A				(  self						: Dict      ):
           # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
           lowerCAmelCase_       :						Any							= 7 if get_gpu_count() > 1 else 2

           lowerCAmelCase_       :						Optional[Any]							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase_       :						int							= F'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

           run_command(self._launch_args + testargs      )
           lowerCAmelCase_       :						Any							= get_results(_UpperCAmelCase      )
           self.assertGreaterEqual(result["""eval_accuracy"""]       ,      0.75      )
           self.assertLess(result["""train_loss"""]       ,      0.5      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """epoch_0"""      )      )      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """ner_no_trainer"""      )      )      )



       @unittest.skip(reason="""Fix me @muellerzr"""      )
       @mock.patch.dict(os.environ       ,      {"""WANDB_MODE""": """offline"""}      )
       def 			A				(  self						: List[Any]      ):
           lowerCAmelCase_       :						Optional[int]							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase_       :						Any							= F'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

           run_command(self._launch_args + testargs      )
           lowerCAmelCase_       :						Union[str, Any]							= get_results(_UpperCAmelCase      )
           # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
           self.assertGreaterEqual(result["""eval_f1"""]       ,      28      )
           self.assertGreaterEqual(result["""eval_exact"""]       ,      28      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """epoch_0"""      )      )      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """qa_no_trainer"""      )      )      )



       @mock.patch.dict(os.environ       ,      {"""WANDB_MODE""": """offline"""}      )
       def 			A				(  self						: int      ):
           lowerCAmelCase_       :						List[str]							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase_       :						List[str]							= F'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

           run_command(self._launch_args + testargs      )
           lowerCAmelCase_       :						Tuple							= get_results(_UpperCAmelCase      )
           self.assertGreaterEqual(result["""eval_accuracy"""]       ,      0.8      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """swag_no_trainer"""      )      )      )



       @slow
       @mock.patch.dict(os.environ       ,      {"""WANDB_MODE""": """offline"""}      )
       def 			A				(  self						: Dict      ):
           lowerCAmelCase_       :						Any							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase_       :						List[str]							= F'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

           run_command(self._launch_args + testargs      )
           lowerCAmelCase_       :						Any							= get_results(_UpperCAmelCase      )
           self.assertGreaterEqual(result["""eval_rouge1"""]       ,      10      )
           self.assertGreaterEqual(result["""eval_rouge2"""]       ,      2      )
           self.assertGreaterEqual(result["""eval_rougeL"""]       ,      7      )
           self.assertGreaterEqual(result["""eval_rougeLsum"""]       ,      7      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """epoch_0"""      )      )      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """summarization_no_trainer"""      )      )      )



       @slow
       @mock.patch.dict(os.environ       ,      {"""WANDB_MODE""": """offline"""}      )
       def 			A				(  self						: Any      ):
           lowerCAmelCase_       :						int							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase_       :						List[Any]							= F'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

           run_command(self._launch_args + testargs      )
           lowerCAmelCase_       :						Tuple							= get_results(_UpperCAmelCase      )
           self.assertGreaterEqual(result["""eval_bleu"""]       ,      30      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """epoch_0"""      )      )      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """translation_no_trainer"""      )      )      )



       @slow
       def 			A				(  self						: List[Any]      ):
           lowerCAmelCase_       :						int							= logging.StreamHandler(sys.stdout      )
           logger.addHandler(_UpperCAmelCase      )

           lowerCAmelCase_       :						List[str]							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase_       :						Any							= F'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

           run_command(self._launch_args + testargs      )
           lowerCAmelCase_       :						str							= get_results(_UpperCAmelCase      )
           self.assertGreaterEqual(result["""eval_overall_accuracy"""]       ,      0.10      )



       @mock.patch.dict(os.environ       ,      {"""WANDB_MODE""": """offline"""}      )
       def 			A				(  self						: Dict      ):
           lowerCAmelCase_       :						Tuple							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase_       :						List[str]							= F'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

           if is_cuda_and_apex_available():
               testargs.append("""--fp16"""      )

           run_command(self._launch_args + testargs      )
           lowerCAmelCase_       :						List[str]							= get_results(_UpperCAmelCase      )
           # The base model scores a 25%
           self.assertGreaterEqual(result["""eval_accuracy"""]       ,      0.6      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """step_1"""      )      )      )
           self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase       ,      """image_classification_no_trainer"""      )      )      )







'''simple docstring'''

import os

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}






def 				a  (     UpperCamelCase_    :    str			)       ->	int:
		snake_case__	     =0

		snake_case__	     =0
		while index < len(UpperCamelCase_			) - 1:
				snake_case__	     =SYMBOLS[numerals[index]]
				snake_case__	     =SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value






def 				a  (     UpperCamelCase_    :    int			)       ->	str:
		snake_case__	     =''

		snake_case__	     =num // 1000
		numerals += m_count * "M"
		num %= 1000

		snake_case__	     =num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		snake_case__	     =num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals






def 				a  (     UpperCamelCase_    :    str = "/p089_roman.txt"			)       ->	int:
		snake_case__	     =0

		with open(os.path.dirname(UpperCamelCase_			) + roman_numerals_filename			) as filea:
				snake_case__	     =filea.readlines()

		for line in lines:
				snake_case__	     =line.strip()
				snake_case__	     =parse_roman_numerals(UpperCamelCase_			)
				snake_case__	     =generate_roman_numerals(UpperCamelCase_			)
				savings += len(UpperCamelCase_			) - len(UpperCamelCase_			)

		return savings


if __name__ == "__main__":
			print(f"""{solution() = }""")




